This repository provides a framework for building voice based applications.
It was created to simplify integrating custom speech services into a website.
It can also be used to build standalone alexa like devices that do not need a network.
Inspired by Snips, the software is provided as a suite of microservices that collaborate using a shared MQTT server. Services include
- audio capture and playback services for local hardware
- audio to text - automated speech recognition(ASR) using streaming for fastest transcriptions. Includes implementations for Deepspeech, IBM Watson and Google
- hotword optimised audio to text using picovoice.
- text to speech (TTS)
- RASA based Natural Language Understanding (NLU) to determine intents and slots from text
- RASA routing using machine learning of stories to translate a history of intents and slots into a choice about the next action.
A sequence of messages passes between the services as the dialog progresses from hotword triggering through speech to text, natural language understanding, routing and finally text to speech in reply to the user.
The software also provides a vanilla javascript library and example for integrating a hotword and visual microphone into a web page as a client of the suite. The client uses mqtt over websockets for live communication and streaming audio back to the hermod server.
The hermod services run in a single threaded asyncio loop for optimimum performance on limited hardware.
Services can be distributed across hardware for high concurrency applications or distributed LAN deployments (satellite mode with pi0)
This project has recently been ported from nodejs to python. In particular on ARM, in my experience, stable packages for speech recognition were more difficult to achieve with nodejs than python. Additionally RASA written in python is a core part of the suite so the portage unifies the development environment for the server side. Access the historic nodejs version remains available via the nodejs branch
The suite provides a Dockerfile to build an image with all os and python dependancies.
The resulting image is available on docker hub as syntithenai/hermod-python.
By default, the image runs all the software required for the suite in a single container.
This repository also provides a docker-compose.yml file to start the suite with services split into many containers.
The image also provides a default set of RASA model files defining configuration, domain, intents, stories and actions for an agent that searches wikipedia.
# install docker
sudo curl -fsSL https://get.docker.com -o get-docker.sh | sh
# install docker-compose
sudo curl -L https://github.com/docker/compose/releases/download/1.22.0/docker-compose-$(uname -s)-$(uname -m) -o /usr/local/bin/docker-compose
# clone this repository
git clone https://github.com/syntithenai/hermod.git
# change directory into it so relative paths in docker-compose.yml to host mounts work correctly
cd hermod
# copy environment from sample (edit as required)
cp .env-sample .env
# start services
sudo docker-compose up
# OR (with pulseaudio on host) to enable local audio
# PULSE_HOST=`ip -4 route get 8.8.8.8 | awk {'print $7'} | tr -d '\n'` ; docker-compose up
Open (https://localhost)[https://localhost] in a web browser.
Say "Hey Edison" or click the microphone button to enable speech and then ask a question.
If local audio is enabled, you can use the hotword "Picovoice" to activate a local dialog session.
The software package has python dependencies that can be installed with
pip install -r requirements.txt
There are also operating system requirements including
- python 3.7+
- nodejs
- installation of a recent version of mosquitto
- pico2wav binary install for the TTS service
- portaudio
- pulseaudio
- download and install deep speech model
- pip install -r requirements.txt in hermod-python/src
- npm install (in hermod-python/tests and hermod-python/rasa/chatito)
See the hermod-python/Dockerfile for install instructions.*
At a bare minimum t3a.micro instance (1 cores, 1G memory) with a 16G root file system
There is not enough memory to train a model on this type of instance so building locally and uploading model files is necessary.
This hardware configuration is usable but significantly compromises the responsiveness.
Newer versions (1.6+) of mosquitto include an option to restrict the header size ```` websockets_headers_size 4096```
When websockets is sharing a domain with a Flask served web application, large cookies cause mosquitto to crash disconnect.
The docker image includes a build of mosquitto 1.6.7
The suite was developed on using Ubuntu Desktop. It should work on most Linux systems. It is largely written in python and requires at least python 3.7
As per the notes below, cross platform shouldn't be too much of a stretch.
-
The TTS service uses a Linux binary pico2wav to generate audio from text. The Google TTS service is cross platform but requires the Internet.
-
For strictly web based services, audio is handled by the client browser so no problems with audio devices. The local Audio Service relies on pyaudio and (optionally) pulseaudio for local microphone capture and playback. Cross platform audio on python is challenging. In particular streaming with asyncio. Implementation on Windows or OSX would require the use of an alternate python sound library that works for those platforms.
-
Raspberry pi4 with ARM runs deepspeech, picovoice and the rest of the hermod suite. However, at this time i haven't been able to install RASA on ARM (although I have in the past) due to missing libraries.
The entrypoint for the source code is the file hermod.py which has a number of command line arguments to enable and disable various features of the software suite.
Environment variables are also used to configure the hermod services.
Using docker-compose to access containers incorporates environment variables from .env
Start a shell in the running web container
docker-compose exec hermod bash
Arguments to hermod.py are mainly used to specify which services should be activated.
Arguments include
- m (--mqttserver) run local mqtt server
- r (--rasaserver) run local rasa server
- w (--webserver) run local web server
- a (--actionserver) run local rasa action server
- d (--hermod) run the hermod services
- sm (--satellite) only run audio and hotword hermod services (for low power devices eg pi0 acting as a satellite that rely on central hermod server)
- nl (--nolocalaudio) skip local audio and hotword services (instead use browser client)
- t (train) train RASA model when starting local server
The entrypoint script hermod.py must be executed from the rasa folder.
For example to start the mosquitto, web and action servers as well as the main hermod services with local audio disabled
cd /app/rasa
python hermod.py -m -w - a -d -nl
Environment variables are used for almost all configurable values needed by services.
When using docker-compose, add environment variables to each services by editing the docker-compose.yml file OR using a .env file in the same folder.
The .env file is excluded from git and is a good place to store secrets. To enable the sample file
cp .env-sample .env
Without docker compose, environment variables should be present in the shell that runs python hermod.py
The admin user credentials are used by the hermod services which listen and respond to messages from many sites (all topics under hermod/) The admin credentials must be provided as environment variables.
MQTT_HOSTNAME: mqtt
MQTT_USER: hermod_server
MQTT_PASSWORD: hermod
MQTT_PORT: 1883
A standalone server with local audio does not require authentication and uses the admin credentials from the environment.
Authentication details are generated for web users when they load the site.
Access to the mqtt server is partitioned by sites. A site corresponds to a mosquitto login user. The mqtt service has access rules so that an authenticated user can read and write to any topic underneath hermod/theirsiteid/
In the example, the web service generates a password when the user logs in and then uses mosquitto_password to update the mosquitto password file via a shared volume with the mosquitto server. The mosquitto server runs an additional thread to watch for changes to its password file and send a HUP signal to mosquitto to trigger a reload when the passwords change. The web server delivers the generated password to the browser client via a templated HTML content.
The deepspeech model files required for speech recognition are not part of this repository.
They are included in the docker image syntithenai/hermod-python available on docker hub.
If you need to download them,
wget -qO- -c https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/deepspeech-0.7.0-models.tar.gz
By default, the model files are expected to be found in in ../deepspeech-models relative to the source directory.
The environment variable DEEPSPEECH_MODELS can be used to set an alternate path.
To enable high quality google speech recognition use console.developers.google.com to create and download credentials for a service account with google speech recognition API enabled. This will require that you enable billing in your google project.
https://console.developers.google.com/
Set environment variables to enable
GOOGLE_APPLICATION_CREDENTIALS=path to downloaded creds file
GOOGLE_APPLICATION_LANGUAGE=optimise recognition for specified language. default en-AU
GOOGLE_ENABLE_ASR=true
If google credentials are provided, the DeepSpeechASR and IBMASR services will be automatically disabled.
22/05/2020 The first 240 (< 15s) requests are free. After than $0.024 USD/minute. Pricing is calculated in 15s increments rounded up. 100 requests costs a minimum of $0.60 USD.
Because most utterances are only a fraction of 15s, the rounding up approach means Google is likely to be more expensive than IBM Watson speech recognition.
Google is noticably more able to accurately capture uncommon words and names than the IBM service ( or deepspeech )
To offload the processing of text to speech generation and for high quality voices, an alternate TTS service implementation using google is provided.
Similarly to google ASR, enable the text to speech API in the google console, download credentials (can be the same file as ASR) and then set environment variables to enable
GOOGLE_APPLICATION_CREDENTIALS=path to downloaded creds file
GOOGLE_APPLICATION_LANGUAGE=optimise recognition for specified language. default en-AU
GOOGLE_ENABLE_TTS=true
22/05/2020 Google charge $4.00 USD per million characters. IBM charge $20/million characters. They also offer a free tier of 10,000 characters per month.
Create resource for speech recognition and download credentials. https://cloud.ibm.com/resources
Set environment variables to enable
IBM_SPEECH_TO_TEXT_APIKEY=your-key-here
IBM_SPEECH_TO_TEXT_REGION=us-east
If ibm credentials are provided, the DeepSpeechASR service will be automatically disabled.
IBM speech to text pricing is calculated as the sum of all audio sent to the service over one month without rounding.
22/05/2020 The free plan provides 500 minutes each month. The standard plan costs $0.0412 USD / minute.
Recent web browsers will not allow access to the microphone unless the connection is made over SSL.
Certificates are generated using certbot when the mqtt service starts. Set environment variables to specify your Internet accessible hostname.
SSL_DOMAIN_NAME=myhost.asuscomm.com
SSL_EMAIL=joe@gmail.com
Mosquitto web sockets is exposed on port 9001 using SSL.
The web server exposes https on port 443 and http on port 80 which redirects to https. These ports can be adjusted (to avoid existing services) using environment variables.
HTTP_PORT=8080
HTTPS_PORT=4430
To enable local audio and hotword services is easiest using the default setup requiring pulse audio.
Depending on your host, you may need to use paprefs or some other method to allow network access to your host pulse audio installation.
To use pulse, the hermod hermod.py file needs to run with
- environment variables PULSE_SERVER and PULSE_COOKIE
- access (? volume mount) to cookie file from host
To populate PULSE_SERVER
export PULSE_SERVER=`ip -4 route get 8.8.8.8 | awk {'print $7'} | tr -d '\n'`
Because this environment variable is the dynamic result of a command, it cannot be placed in the shared .env file but needs to be set in the host shell that runs docker-compose (and pulse) (Unless the ip is truly static)
The docker file includes a host volume mount to /$HOME/.pulse/cookie as /tmp/cookie and sets PULSE_COOKIE=/tmp/cookie ``` ${HOME}/.config/pulse/cookie:/tmp/cookie ````
It is possible to configure hermod to use any ALSA hardware device rather than the default pulse device.
Use environment variables to specify which ALSA hardware device to use.
eg
- MICROPHONE_DEVICE=dmix
- SPEAKER_DEVICE=dmix
Depending on your ALSA configuration (/etc/asound.conf), different devices may be available.
Hermod requires microphone audio to be delivered as 16000, 1channel. ALSA config allows for virtual remixed devices.
The speaker channel needs to be able to convert from any sound format.
Depending on your configuration, access to the sound card may be restricted to one process (dmix can help)
The docker images includes alsa config files to enable pulse
- ./pulseaudio/asound-pulse.conf => /etc/asound.conf
- ./pulseaudio/client.conf => /etc/pulse/client.conf
When using docker without pulse, these files will need to be customised using volume mounts or by rebuilding the image.
hermod.py can be used alongside an existing RASA installation by providing a url the the HTTP API. The environment variable RASA URL is required.
RASA_URL: http://rasa:5005
It can also be used to start a RASA HTTP server using the -r argument. The environment variables DUCKLING_URL and RASA_ACTIONS_URL are required. Notably,the duckling URL is built into the RASA model when it is trained and the built value must match the environment variable. If RASA_ACTIONS_URL is present in the environment when starting hermod.py, the endpoints.yml file is updated to set the action_endpoint.url to match the environment variable when RASA starts.
DUCKLING_URL: http://duckling:8000
RASA_ACTIONS_URL: http://hermod:5055/webhook
For optimum performance, the RasaLocalService can be used which skips the HTTP server and loads and queries the RASA model directly.
A path to the model file must be provided.
RASA_MODEL: /app/rasa/models/model.tar.gz
In a development environment it is ideal to run the RASA HTTP server in a different container to avoid restarts for code changes.
The hermod.py script provides options for training.
In the RASA folder data/nlu.md provides fixed training data
The folder chatito includes many files which are used to generate training data into the file chatito/nlu.md
To generate training data from the chatito files.
docker-compose exec hermod ../src/hermod.py -g
To train the model using both fixed and chatito training data.
docker-compose exec hermod ../src/hermod.py -t
Building a good model requires lots of samples. While generation from a DSL runs the risk of overfitting if comprehensive data sets are provided, samples of a generated data set can be helpful in quickly building initial training and testing data.
In particular entity matching from a large set defined as a lookup file, benefits from (integrating more samples of lookup values)[https://blog.bitext.com/improving-rasas-results-with-artificial-training-data-ii]
Developing with hermod is mainly developing with RASA. Building/training a model and implementing actions.
By default, dynamic actions are implemented using a local RASA action server. An actions.py file in the rasa folder includes classes that satisfy the Action api.
Any text messages returned by RASA are collated and a hermod/siteid/tts/say message is sent by the dialog manager.
Because hermod runs in the context of an mqtt server, actions can also communicate with the client in real time by sending messages. For example, the action can send an mqtt message to the topic hermod/myhsite/tts/say to have speech generated and spoken immediately (eg looking now) while the action continues to collate and process data before giving a final response.
The AudioService and the javascript client send an initialisation message hermod/site>/dialog/init with a JSON payload including information about the client including supported features and platform.
The DialogManagerService listens for these messages and sends appropriate activate and start messages for asr, hotword and microphone.
The TTS services also listens for these messages and caches the client information so that clients who have registered via a web platform are sent TTS audio as a url rather than the default of splitting into mqtt audio packets for final reassembly and playback. Streaming playback using MQTT by reconstructing audio streams is difficult. A web server is designed for the job.
The RASA service also listens for these messages and saves the payload as a slot hermod_client so that the information is available to custom actions to respond based on supported features of the client.
In a speech dialog, a conversation can end and switch the microphone back to hotword mode OR it can continue and leave the microphone active for a response from a user.
The default is set by the environment variable HERMOD_KEEP_LISTENING=true
More fine tuned control can be applied through stories or custom actions.
When hermod is configured to keep listening, an action_end as the last action in your story will force the microphone to return to the hotword.
## say goodbye
* quit
- utter_goodbye
- action_end
When hermod is configured not to keep listening, action_continue can be used as the last action to force the microphone to restart for an intent that needs further input
## save fact success
* save_fact{"attribute": "meaning","thing": "life","answer": "42"}
- action_confirm_save_fact
- slot{"attribute": "meaning"}
- slot{"thing": "life"}
- slot{"answer": "42"}
- action_continue
* affirmative
- action_save_fact
NOTE These actions will need to added to your domain file and enabled for the action server.
Where the story does not force the issue, custom actions can use a slot to force the microphone status.
slotsets.append(SlotSet("hermod_force_continue", "true"))
or
slotsets.append(SlotSet("hermod_force_end", "true"))
If both slots are set, hermod_force_continue takes precedence.
NOTE These slots need to be added to your domain file
For fallback actions, a sample implementation of action_default_fallback is included with the action server that sets the slot to force the microphone to restart.
After a period of silence or failed recognition attempts, the microphone will turn itself back to hotword mode.
Any mqtt client (mqttbox, hbmqtt, mosquitto_sub) can be used to show communication between the services as a dialog progresses.
Using the mqtt admin credentials from your environment file,
mosquitto_sub -v -u hermod_server -P hermod -t hermod/+/dialog/# -t hermod/+/asr/# -t hermod/+/tts/# -t hermod/+/hotword/# -t hermod/+/speaker/# &
mosquitto_sub -v -u hermod_server -P hermod -t hermod/+/dialog/# -t hermod/+/asr/# -t hermod/+/tts/# -t hermod/+/hotword/# -t hermod/+/speaker/# &
mosquitto_sub -v -u hermod_server -P hermod -t hermod/+/dialog/# -t hermod/+/asr/# -t hermod/+/nlu/# -t hermod/+/core/# -t hermod/+/tts/# -t hermod/+/hotword/# -t hermod/+/speaker/start -t hermod/+/speaker/stop -t hermod/+/rasa/# &
An example web application answers questions usings wikipedia and other sources.
The hermod web client in hermod-python/www/static/bundle.js provides a vanilla javascript library for starting and stopping hotword and speech recognition/audio streaming in a browser.
The example applications provides dual implementations using either vanilla javascript or using React.
If using the library with vanilla javascript, changes to the library will require a rebuild using watchify.
watchify index.js -o static/bundle.js
The file hermod-python/www/index.html shows vanilla usage.
The file hermod-python/www/spokencrossword/src/HermodClient.js shows how the vanilla script can be wrapped into a React component.
First construct a client with configuration as follows. In the following example, email, password and site are generated from server template variables in python.
var config = {
server: protocol + window.location.hostname + ':' + port,
username: "{{data.get('email_clean')}}",
password: "{{data.get('password')}}",
subscribe: "hermod/{{data.get('email_clean')}}/#",
hotwordsensitivity : 0.5 ,
site:"{{data.get('email_clean')}}"
}
then connect and start the hotword service
client = new window.HermodWebClient(config)
client.connect().then(function() {
client.startHotword()
})
Once connected the client listens for all messages in its site ie hermod/mysite/#
The client responds to the following messages
- hermod/mysite/asr/text
All messages in the subtopic are available by binding to the message event using the client bind method.
client.bind('message',function(message,payloadIn) {})
The client exposes methods including
volume management
-
setVolume
-
muteVolume
-
unmuteVolume
-
playSound
-
stopPlaying
-
stopAll
-
say
Bind and unbind events including microphoneStart,microphoneStop,hotwordStart,hotwordStop,disconnect,connect,speaking,stopspeaking,message
-
bind
-
unbind
-
sendAndWaitFor
-
sendAudioAndWaitFor
-
sendMessage
-
sendNLUMessage
-
sendASRTextMessage
-
connect
-
disconnect
-
startHotword
-
stopHotword
-
startMicrophone
-
stopMicrophone
TODO update test suite to latest image and features.
The test suite was developed with nodejs and npm. jest is used as a testing library for hermod-nodejs.
The test suite was then used to facilitate development of the python version.
The tests require a docker image syntithenai/hermod-python to provide hermod in a python 3.7 environment with os dependancies installed and default models installed.
cd hermod/tests
npm install
npm test
TODO update the following message reference for recent changes
Each dialog session is assigned an id which is passed with each subsequent request in the dialog.
An id is created (if missing) when the dialog manager receives dialog/start, dialog/continue, asr/text, nlu/intent messages.
Both local and web AudioServices buffer captured audio.
To minimise network traffic, voice detection algorithms are used to enable and disable streaming of audio packets.
When voice detection hears speech, the buffered audio is sent before starting to stream packets.
When voice detection hears no speech for a short period, audio streaming is paused.
The media service can play and record audio on a device and send or receive it from the MQTT bus.
The ASR and Hotword services listen for audio via the MQTT bus. The TTS service sends audio file of generated speech to the MQTT bus.
This means that the ASR and Hotword services do not work unless the microphone service is started with hermod/<siteId>/microphone/start
To minimise traffic on the network, the dialog manager enables and disables media streaming in response to lifecycle events in the protocol. In particular, the dialog manager ensures audio recording is enabled or disabled in sync with the ASR or Hotword services. However when the suite is configured to keep the hotword enabled at all times, the microphone is left enabled as well.
The microphone service also implements silence detection and pauses sending packets when there is no voice detected.
Incoming
-
hermod/<siteId>/speaker/play- Play the wav file on the matching siteId.
-
hermod/<siteId>/speaker/stop- Stop playing all current audio output.
-
hermod/<siteId>/speaker/volume- Set the volume for current and future playback.
-
/hermod/<siteId>/microphone/start- Start streaming audio packets
-
hermod/<siteId>/microphone/stop- Stop streaming audio packets
Outgoing
-
hermod/<siteId>/speaker/finished- Sent when the audio from a play request has finished playing on the hardware.
-
hermod/<siteId>/microphone/audio- Sent continuously when microphone is started.
- Message contains audio packet (Format 1 channel, 16 bit, 16000 rate)
A hotword recogniser is a special case of automated speech recognition that is optimised to recognising just a few phrases. Optimising for a limited vocabulary means that the recognition engine can use minimum memory and resources.
The hotword recogniser is used in the protocol to initiate a conversation.
The hotword service listens for audio via the MQTT bus. When the hotword is detected a message is sent to the bus in reply.
If the service is enabled for the site, hermod//hotword/detected is sent.
Incoming
hermod/<siteId>/hotword/starthermod/<siteId>/hotword/stop
Outgoing
hermod/<siteId>/hotword/detected- Sent when service is enabled and hotword is detected.
- JSON message body
- hotword - identifier for the hotword that was heard.
The ASR service converts audio data into text strings. The service listens on the MQTT bus for audio packets.
When the ASR detects a long silence (XX sec) in the audio stream, the final transcript is sent and the ASR service clears it's audio transcription buffer for the site.
The software provides two alternate ASR services
- deepspeech local. Less accurate, slower. No Internet required.
- Google ASR. Faster, more accurate. Requires Internet.
TODO Explore the possibilities of running both concurrently in 'economy mode' where HD ASR from google is activated after a misunderstanding or explicitly for filling text slots.
From the previous version which included more ASR engines
ASR is the most computationally expensive element of the protocol. Some of the implementations described below require more processing power and memory than is available on a Raspberry Pi. In particular running multiple offline models is likely to be unresponsive on low power machines.
Open source implementations of ASR include Kaldi, Mozilla DeepSpeech and PocketSphinx.
Closed source implementations include Snips, Google and Amazon Transcribe.
Snips has the advantage being optimised minimum hardware and for of providing a downloadable model so transcription requests can be run on local devices (including Raspberry Pi).
The ASR service allows the use of a suite of ASR processor implementations where each model is customised. The
asrModelparameter of an ASR start message allows switching between models on the fly.
- Snips provides a reasonable quality general model but works best when the using the web UI to create a specific ASR model.
- Google or Amazon offer the best recognition accuracy because of access to large voice data sets and would be more appropriate for arbitrary transcription.
- The open source solutions are not quite as accurate as the commercial offerings citing WER under 10% which approaches the human error rate of 5.83 and works very well when combined with NLU.
Some implementations perform recognition once off on an audio fragment. Other implementations allow for streaming audio and sending intermediate recognition results.
ASR implementations from Google and Amazon provide punctuation in results.
Google also implements automatic language(en/fr/jp) detection and provides a request parameter to select background noise environment.
As at 28/12/18, Amazon and Google charge $0.006 AUD / 15 second chunk of audio.
Depending on the implementation, the ASR model can be fine tuned to the set of words you want to recognise.
- Snips provides a web UI to build and download models
- Google allows phraseHints to be sent with a recognition request.
- Amazon offers an API or web UI to develop vocabularies in addition to the general vocabulary.
- The open source implementations Deepspeech and Kaldi offer examples of training the ASR model.
For some implementations, a pool of ASR processors is managed by the service to support multiple concurrent requests. In particular, implementation using Kaldi provides this feature using gstreamer.
Incoming
-
hermod/<siteId>/asr/start- Start listening for audio to convert to text.
- JSON body with
- id - (optional) unique id sent forwarded with results to help client connect result with original request
-
hermod/<siteId>/asr/stop- Stop listening for audio to convert to text.
Outgoing
-
hermod/<siteId>/asr/started -
hermod/<siteId>/asr/stopped -
hermod/<siteId>/asr/partial- Send partial text results
- JSON body with
- id
- text - transcribed text
-
hermod/<siteId>/asr/text- Send final text results
- JSON body with
- id
- text - transcribed text
The NLU service parses text to intents and variable slots.
Custom models can be developed and trained using a web user interface (based on rasa-nlu-trainer) or text files.
The NLU model is configured with slots. When slots are extracted, the processing pipeline may be able to transform the values and extract additional metadata about the slot values. For example converting "next tuesday" into a Date or recognising a value in a predefined slot type.
Parsing results are sent to hermod/nlu/intent as a JSON message. For example
{ "intent": { "name": "restaurant_search", "confidence": 0.8231117999072759 }, "entities": [ { "value": "mexican", "raw": "mexican", "entity": "cuisine", "type": "text" } ], "intent_ranking": [ { "name": "restaurant_search", "confidence": 0.8231117999072759 }, { "name": "affirm", "confidence": 0.07618757211779097 }, { "name": "goodbye", "confidence": 0.06298664363805719 }, { "name": "greet", "confidence": 0.03771398433687609 } ], "text": "I am looking for Mexican food" }
The NLU service is implemented using RASA. RASA configuration allows for a pipeline of processing steps that seek for patterns and extract metadata. Initial steps in the pipeline prepare data for later steps.
Incoming
hermod/<siteId>/nlu/parse- Convert a sentence into intents and slots
- With JSON body
- text - sentence to convert into intents and slots
Outgoing
-
hermod/<siteId>/nlu/intent- Send parsed intent and slots
-
hermod/<siteId>/nlu/fail- Send when entity recognition fails because there are no results of sufficient confidence value.
The dialog manager coordinates the services by listening for MQTT messages and responding with MQTT messages to further the dialog.
The dialog manager tracks the state of all active sessions so that it can
- Send fallback messages if services timeout.
- Garbage collect session and access data.
- Log analytics data.
Outgoing messages are shown with => under the related incoming message.
hermod/<siteId>/hotword/detectedhermod/<siteId>/dialog/start- Start a dialog
- =>
hermod/<siteId>/hotword/stop - =>
hermod/<siteId>/microphone/start - =>
hermod/<siteId>/asr/start - =>
hermod/<siteId>/dialog/started/<dialogId>
** Where a dialog/start message includes a non empty text parameter in the message body, the dialog manager skips ASR and jumps to NLU **
-
hermod/<siteId>/dialog/start{text:'text sent directly'}- => hermod//hotword/stop`
- => hermod//microphone/stop`
- =>
hermod/<siteId>/nlu/parse{text:'text sent directly'} - =>
hermod/<siteId>/dialog/started/<dialogId>
-
hermod/<siteId>/dialog/continue- Sent by an action to continue a dialog and seek user input.
- JSON body
- text - text to speak before waiting for more user input
- => hermod//microphone/stop
- => hermod//tts/say
- After hermod//tts/finished - => hermod//microphone/start - => hermod//asr/start
-
hermod/<siteId>/asr/text- => hermod//nlu/parse
-
hermod/<siteId>/nlu/intent- => hermod//intent
-
hermod/<siteId>/dialog/end- The application that is listening for the intent, should send
hermod/<siteId>/dialog/endwhen it's action is complete so the dialog manager can garbage collect dialog resources. - Respond with
hermod/<siteId>/dialog/endedhermod/<siteId>/microphone/starthermod/<siteId>/hotword/start
- The application that is listening for the intent, should send
The dialog manager is the glue between the services.
Service output messages are consumed by the dialog manager which then sends another message to the next service in the stack.
Because mediation by the dialog manager is required at each step in the dialog flow, it is able to track and control the state of each dialog to ensure valid dialog flow and manage asynchronous collation of dialog components before some stages in the dialog.
In general, a service should send message to let the dialog manager know when it starts and stops.
When the dialog manager starts, it sends
hermod/<siteId>/microphone/starthermod/<siteId>/hotword/start
When a session is initiated by one of
hermod/<siteId>/hotword/detectedhermod/<siteId>/dialog/start
The dialog manager creates a new dialogId, then sends a series of MQTT messages to further the dialog.
hermod/<siteId>/microphone/starthermod/<siteId>/dialog>/asr/starthermod/<siteId>/dialog/started
The ASR sends hermod/<siteId>/asr/started and when the ASR finishes detecting text it sends hermod/<siteId>/text with a JSON payload.
The dialog manager hears this message and sends with a text message to speak in the JSON body (For example asking a question)
hermod/<siteId>/asr/stophermod/<siteId>/nlu/parse
The NLU service hears the parse request and sends
hermod/<siteId>/nlu/startedhermod/<siteId>/nlu/intentorhermod/<siteId>/nlu/fail.
The dialog manager hears the nlu intent message and sends
hermod/<siteId>/intent
The core application router hears the intent message and starts an action processing loop by asking rasa core to determine the next action recursively until the next action is either action_listen or action_end. At each step the core routing service sends a hermod/<siteId>/action message with a JSON body including the action and currently tracked slots.
The application service hears each action message and runs. When finished it sends hermod/<siteId>/action/finished.
The core routing service processes each action sequentially (by waiting for action/finished Message) and when the loop finishes, it sends messages to hand the dialog back to the user. When the final action is action_listen, the service sends hermod/<siteId>/dialog/continue. If the last action is action_end, the service sends hermod/<siteId>/dialog/end
The dialog manager hears the continue message and sends
hermod/<siteId>/microphone/starthermod/<siteId>/asr/start
to restart voice recognition
OR
The dialog manager hears the end message. (This can be issued at any time). It clears the audio buffer and sends
hermod/<siteId>/microphone/starthermod/<siteId>/dialog/endedhermod/<siteId>/hotword/start
to finish the dialog and enable the hotword detector.
The text to speech service generates audio data from text. Audio containing the spoken text is sent to the media service via the MQTT bus.
Offline TTS implementations include Mycroft Mimic, picovoice, MaryTTS, espeak, merlin or speak.js in a browser.
Online TTS implementation include Amazon Polly and Google. These services support SSML markup.
Incoming
hermod/<siteId>/tts/say
- Speak the requested text by generating audio and sending it to the media streaming service.
- Parameters
- text - text to generate as audio
- lang - (optional default en_GB) language to use in interpreting text to audio
hermod/<siteId>/speaker/finished
- When audio has finished playing send a message to hermod//tts/finished to notify that speech has finished playing.
Outgoing
hermod/<siteId>/speaker/play/<speechRequestId>
- speechRequestId is generated
- Body contains WAV data of generated audio.
hermod/<siteId>/tts/finished
- Notify applications that TTS has finished speaking.
-
generate crossword - also puz format for dl
-
search google for ...
-
story logging
-
osx bugs ?
-
oauth login
-
disambiguate wiki searches
-
quiz (like mnemo alexa quiz)
-
crossword tools - clues, anagrams, synonyms
-
crossword builder tool - select text from proxy scrape sites
-
jovo adapter
-
hbmqtt
-
pi4 rasa
-
intent filters ?
-
media crossword - bird sounds
-
unify entities person, place, thing -> thing
-
rasa 2 stage fallback https://rasa.com/docs/core/master/policies/#two-stage-fallback-policy
-
Sample music player web application
-
music player model
- play some {tag}
- play something by {artist}
- stop
- volume
- start
-
deepspeech asr component frequency filtering (as per deepspeech nodejs example)
-
Service Monitoring (TODO) ? partially present The dialog manager tracks the time duration between some messages so it can determine if services are not meeting performance criteria and provide useful feedback. Where a services is deemed unresponsive, an error message is sent and the session is ended by sending
hermod/<siteId>/dialog/end. Services are considered unresponsive in the following circumstances
- For the ASR service, If the time between asr/start until asr/text exceeds the configured maximumDuration
- For the ASR service, If the time from ASR starting and then silence being detected , to asr/text or asr/fail exceeds the configured asrTimeout.
- For the NLU service, If the time between nlu/parse and nlu/intent exceeds the configured nluTimeout
- For the core routing service, If the time between nlu/intent and hermod/intent exceeds the configured coreTimeout
- For the TTS service, if the time between tts/say and tts/finished
- For the media streaming service, if the time between speaker/play and speaker/finished
-
load testing.
-
chomecast web client
-
capture training data
- NLU DONE
- stories
- integrate into base training data
hermod.py -t
-
HDASR
- single service that switches between google and deepspeech
- explicitly triggered by actions for a single following utterance. Useful for capturing arbitrary text to a slot.
- triggered when NLU fails and calls action_default (or 2 stage fallback)
- enable Google with daily limits
- single service that switches between google and deepspeech
-
auto adjust to ambient volume (per mycroft)
-
RASA
-
Dialog Extensions (TODO)???
The dialog manager tracks when multiple ASR or NLU services of the same type indicate that they have started. It waits for all final responses and selects the highest confidence before sending the next message.
For example, when two ASR services on the same bus share a model key and respond to hermod/<siteId>/asr/start sending two hermod/<siteId>/asr/started messages, the dialog manager waits for both to respond with hermod/<siteId>/asr/text before sending hermod/<siteId>/nlu/parse.
When two NLU services indicate they have started , the dialog manager waits before sending hermod/<siteId>/intent.
When Voice ID is enabled, the dialog manager waits for hermod/<siteId>/voiceid/detected/<userId> before sending hermod/<siteId>/intent.
When multiple devices in a room hear an utterance, they all respond at the same time. This affects Google Home, Alexa, Snips and Mycroft. Google has elements of a solution because an Android phone will show a notification saying "Answering on another device". Two Google Home devices in a room will both answer.
The hermod protocol with many satellites sharing a dialog service allows the solution that the hotword server could be debounced.
When the dialog manager hears 'hermod//hotword/detected' or 'hermod//dialog/start', it waits for a fraction of a second to see if there are any more messages with the same topic, where there are multiple messages, the one with the highest confidence/volume is selected and the others are ignored.
?? The debounce introduces a short delay between hearing a hotword and starting transcription. To avoid requiring the user pause after the hotword, the ASR needs audio from immediately after the hotword is detected and before transcription is started. To support this, the media server maintains a short ring buffer of audio that is sent before audio data from the hardware. The length of audio that is sent can be controlled by a parameter prependAudio in the JSON body of a message to hermod//microphone/start
-
Contribute to the Mozilla Open Source Voice Dataset)[https://voice.mozilla.org/en/speak]
-
Hotwords
- (picovoice)[https://picovoice.ai/] (also for web browsers)
- (snowboy)[https://snowboy.kitt.ai/]
-
Automated Speech to Text Recognition (ASR)
- (CMUSphinx)[http://cmusphinx.github.io/]
- (Kaldi)[https://kaldi-asr.org/]
- (Deepspeech)[https://github.com/mozilla/DeepSpeech]
- (Facebook wav2letter)[https://github.com/facebookresearch/wav2letter]
-
Text to Speech (TTS)
- (pico2wav)[https://packages.debian.org/jessie/libttspico0]
- (mycroft mimic)[https://mycroft-ai.gitbook.io/docs/mycroft-technologies/mimic-overview]
- (mycroft mimic 2)[https://github.com/MycroftAI/mimic2#mimic2]
-
Natural Language Understanding (NLU) Service
- (RASA)[https://rasa.com/docs/rasa/nlu/about/]
- (Duckling)[https://github.com/facebook/duckling]
- (Snips NLU)[https://github.com/snipsco/snips-nlu]
- (Mycroft Adapt)[https://mycroft-ai.gitbook.io/docs/mycroft-technologies/adapt]
- (Mycroft Padatious)[https://mycroft-ai.gitbook.io/docs/mycroft-technologies/padatious]
-
NLU Tools
- (Apache NLP)[https://opennlp.apache.org/]
-
(Stanford NLP)[https://stanfordnlp.github.io/CoreNLP/]
-
Dialog Flow/Routing
- (RASA Core)[https://rasa.com/docs/rasa/core/about/]
-
Other
- (RASA Voice Interface)[https://github.com/RasaHQ/rasa-voice-interface] - Integrate RASA and deepspeech into web browser without intermediate service makes Deepspeech websocket requests and RASA calls from the browser.
- (JOVO Framework)[https://www.jovo.tech/] -(Deepspeech on AWS Serverless)[https://github.com/samfeder/banter-deepspeech/blob/master/serverless.yml]
- https://jasperproject.github.io/documentation/modules/ - voice module plugins for movies, stocks, ...
- http://doc.tock.ai/tock/en/ - java/kotlin bot framework
- https://picovoice.ai/tutorials/using-picovoice-engines-with-react/.